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In this contribution, we propose a data-driven procedure to fitquadratic-bilinear surrogate models from data. Although the dynamicscharacterizing the original model are strongly nonlinear, we rely on liftingtechniques to embed the original model into a quadratic-bilinear format. Here,data represent generalized transfer function values. This method is anextension of methods that do bilinear, or quadratic inference, separately. Itis based on first fitting a linear model with the classical Loewner framework,and then on inferring the best supplementing nonlinear operators, in aleast-squares sense. The application scope of this method is given byelectrical circuits with nonlinear components (such as diodes). We proposevarious test cases to illustrate the performance of the method.

A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits